Artificial Intelligence in Forecasting Antibody-drug Conjugate Efficacy in Oncology: A Narrative Review

Introduction: Artificial Intelligence (AI) is reshaping the healthcare system by enhancing drug development, precise medical care, and the prognosis of the therapy. Antibody-drug conjugates (ADCs) are an upcoming type of oncotherapy that uses monoclonal antibodies to deliver cytotoxic therapeutic agents directly into tumour cells. ADCs reduce the side effects which are generally encountered with traditional chemotherapy. Method: AI analyses clinical and genetic data, via machine learning (ML) and deep learning (DL), to anticipate patient response to various ADCs. AI-based software and databases use genomic, proteomic, and clinical trial data to envisage the response of cancer patients to the ADC therapies. Conclusion: This review explains the use of AI to predict ADC efficacy, patient selection and therapy outcome. AI techniques, including ML and DL, analyse large datasets and biomarkers to improve targeted therapies. It also explores the drawbacks of AI in healthcare and how it can be managed to ensure positive outcomes of AI in healthcare.